Dynamic Formation Reshaping Based on Point Set Registration in a Swarm of Drones
It addresses formation control for autonomous drone swarms in constrained environments, representing an incremental improvement with specific applications.
This work tackles the problem of dynamically reshaping formations in drone swarms to navigate through narrow openings, introducing routines for optimal reshaping and using a temperature function reduction technique to minimize deviation and time, achieving near-optimal performance.
This work focuses on the formation reshaping in an optimized manner in autonomous swarm of drones. Here, the two main problems are: 1) how to break and reshape the initial formation in an optimal manner, and 2) how to do such reformation while minimizing the overall deviation of the drones and the overall time, i.e., without slowing down. To address the first problem, we introduce a set of routines for the drones/agents to follow while reshaping to a secondary formation shape. And the second problem is resolved by utilizing the temperature function reduction technique, originally used in the point set registration process. The goal is to be able to dynamically reform the shape of multi-agent based swarm in near-optimal manner while going through narrow openings between, for instance obstacles, and then bringing the agents back to their original shape after passing through the narrow passage using point set registration technique.